86 lines
2.5 KiB
Python
86 lines
2.5 KiB
Python
"""Entry point for running the evolution strategy on the benchmark function."""
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from __future__ import annotations
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import numpy as np
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from es import EvolutionStrategyConfig, run_evolution_strategy
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from functions import axis_parallel_hyperellipsoid, default_bounds
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def run_for_dimension(
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dimension: int,
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*,
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results_dir: str,
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max_generations: int = 200,
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seed: int | None = 17,
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save_generations: list[int] | None = None,
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log: bool = False,
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):
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x_min, x_max = default_bounds(dimension)
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search_range = x_max - x_min
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initial_sigma = np.full(dimension, 0.15 * search_range[0], dtype=np.float64)
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config = EvolutionStrategyConfig(
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fitness_func=axis_parallel_hyperellipsoid,
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dimension=dimension,
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x_min=x_min,
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x_max=x_max,
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mu=20,
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lambda_=80,
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mutation_probability=0.7,
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initial_sigma=initial_sigma,
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max_generations=max_generations,
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selection="comma",
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recombination="intermediate",
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parents_per_offspring=2,
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success_rule_window=5,
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success_rule_target=0.2,
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sigma_increase=1.22,
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sigma_decrease=0.82,
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sigma_scale_min=1e-3,
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sigma_scale_max=50.0,
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sigma_min=1e-5,
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sigma_max=2.0,
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best_value_threshold=1e-6,
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max_stagnation_generations=40,
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save_generations=save_generations,
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results_dir=results_dir,
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log_every_generation=log,
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seed=seed,
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)
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result = run_evolution_strategy(config)
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print("=" * 80)
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print(f"Результаты для размерности n={dimension}")
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print(f"Лучшее решение: {result.best_generation.best.x}")
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print(f"Лучшее значение функции: {result.best_generation.best.fitness:.8f}")
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print(f"Количество поколений: {result.generations_count}")
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print(f"Время выполнения: {result.time_ms:.2f} мс")
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print("=" * 80)
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return result
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def main() -> None:
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# Для n=2 дополнительно сохраняем графики поколений
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run_for_dimension(
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2,
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results_dir="lab5_results_2d",
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save_generations=[1, 2, 3, 5, 8, 10, 15, 20, 25, 30, 40, 50, 75, 100, 150, 200],
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log=True,
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)
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# Для n=3 графики не строим, но выводим статистику
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run_for_dimension(
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3,
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results_dir="lab5_results_3d",
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save_generations=None,
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log=False,
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)
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if __name__ == "__main__":
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main()
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